Sparsity and cosparsity for audio declipping: a flexible non-convex approach

TL;DR

This paper compares sparse synthesis and analysis methods for audio declipping, proposing a flexible non-convex approach that performs well and enables real-time processing, outperforming existing techniques especially on severely saturated signals.

Contribution

It introduces a versatile non-convex heuristic applicable to both data models, demonstrating comparable performance and real-time capability for analysis-based declipping.

Findings

Both models perform similarly in signal enhancement.

Analysis model enables real-time audio processing.

Outperforms state-of-the-art methods on severely saturated signals.

Abstract

This work investigates the empirical performance of the sparse synthesis versus sparse analysis regularization for the ill-posed inverse problem of audio declipping. We develop a versatile non-convex heuristics which can be readily used with both data models. Based on this algorithm, we report that, in most cases, the two models perform almost similarly in terms of signal enhancement. However, the analysis version is shown to be amenable for real time audio processing, when certain analysis operators are considered. Both versions outperform state-of-the-art methods in the field, especially for the severely saturated signals.